Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Multi-Agent Motion Planning on Industrial Magnetic Levitation Platforms: A Hybrid ADMM-HOCBF approach

Bavo Tistaert, Stan Servaes, Alejandro Gonzalez-Garcia, Ibrahim Ibrahim, Louis Callens, Jan Swevers, Wilm Decré

Abstract

This paper presents a novel hybrid motion planning method for holonomic multi-agent systems. The proposed decentralised model predictive control (MPC) framework tackles the intractability of classical centralised MPC for a growing number of agents while providing safety guarantees. This is achieved by combining a decentralised version of the alternating direction method of multipliers (ADMM) with a centralised high-order control barrier function (HOCBF) architecture. Simulation results show significant improvement in scalability over classical centralised MPC. We validate the efficacy and real-time capability of the proposed method by developing a highly efficient C++ implementation and deploying the resulting trajectories on a real industrial magnetic levitation platform.

Multi-Agent Motion Planning on Industrial Magnetic Levitation Platforms: A Hybrid ADMM-HOCBF approach

Abstract

This paper presents a novel hybrid motion planning method for holonomic multi-agent systems. The proposed decentralised model predictive control (MPC) framework tackles the intractability of classical centralised MPC for a growing number of agents while providing safety guarantees. This is achieved by combining a decentralised version of the alternating direction method of multipliers (ADMM) with a centralised high-order control barrier function (HOCBF) architecture. Simulation results show significant improvement in scalability over classical centralised MPC. We validate the efficacy and real-time capability of the proposed method by developing a highly efficient C++ implementation and deploying the resulting trajectories on a real industrial magnetic levitation platform.
Paper Structure (22 sections, 23 equations, 4 figures, 2 tables, 1 algorithm)

This paper contains 22 sections, 23 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries and problem formulation
  3. Conventions
  4. High-Order Control Barrier Functions
  5. Problem formulation
  6. Hybrid method and algorithm
  7. ADMM
  8. ADMM-HOCBF
  9. Pseudo-code
  10. Rectangular arena constraints
  11. Corridor strategy for nonconvex arenas
  12. Results and experimental validation
  13. Simulation results
  14. Scalability analysis
  15. Performance comparison and safety
  16. ...and 7 more sections

Figures (4)

  • Figure 1: The Beckhoff XPlanar setup in the robotics lab. This industrial machine is used for the experimental validation of the motion planner.
  • Figure 2: To deal with nonconvex arenas, agents track a sequence of sub-targets before arriving at their main target. The sub-targets are located in the intersection of corridors, where agents can switch from one corridor to the next. The final sub-target is the destination of the agent.
  • Figure 3: Scalability plot of ADMM-HOCBF ($m=1$ and $m=20$) and centralised MPC.
  • Figure 4: Trajectories of 5 agents on the XPlanar system with ADMM-HOCBF (m=20).

Theorems & Definitions (10)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Remark 1
  • Remark 2
  • Remark 3
  • Remark 4
  • Remark 5
  • Remark 6